5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness.

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The generalized estimating equation (GEE) approach can be used to analyze cluster randomized trial data to obtain population-averaged intervention effects. However, most cluster randomized trials have some missing outcome data and a GEE analysis of available data may be biased when outcome data are not missing completely at random. Although multilevel multiple imputation for GEE (MMI-GEE) has been widely used, alternative approaches such as weighted GEE are less common in practice. Using both simulations and a real data example, we evaluate the performance of inverse probability weighted GEE vs. MMI-GEE for binary outcomes. Simulated data are generated assuming a covariate-dependent missing data pattern across a range of missingness clustering (from none to high), where all covariates are measured at baseline and are fully observed (i.e. a type of missing-at-random mechanism). Two types of weights are estimated and used in the weighted GEE: (1) assuming no clustering of missingness (W-GEE) and (2) accounting for such clustering (CW-GEE). Results show that, even in settings with high missingness clustering, CW-GEE can lead to more bias and lower coverage than W-GEE, whereas W-GEE and MMI-GEE provide comparable results. W-GEE should be considered a viable strategy to account for missing outcomes in cluster randomized trials.

          Related collections

          Author and article information

          Journal
          Stat Methods Med Res
          Statistical methods in medical research
          SAGE Publications
          1477-0334
          0962-2802
          May 2020
          : 29
          : 5
          Affiliations
          [1 ] Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
          [2 ] Duke Global Health Institute, Duke University, Durham, NC, USA.
          [3 ] Department of Population Health, New York University, New York, NY, USA.
          [4 ] INRIA SISTM, Inserm U1219 Bordeaux Population Health, Université Bordeaux, ISPED, Bordeaux, France.
          [5 ] Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
          Article
          10.1177/0962280219859915
          31293199
          7f9e8657-8e3d-483a-82d1-6b2c9d0b1697
          History

          missing data,multilevel multiple imputation,inverse probability weights,cluster randomized trial,Generalized estimating equations

          Comments

          Comment on this article